Autonomous In-Row Navigation of an Orchard Robot with a 2D LIDAR Scanner and Particle Filter with a Laser-Beam Model

Blok, Pieter M.; Suh, Heikyung; Boheemen, Koen van; Kim, Hak-Jin; Kim, Gookhwan

doi:10.5302/j.icros.2018.0078

Cited by 5 publications

(3 citation statements)

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“…The robot deflection is determined by a combination of visual guidance and LiDAR. The robot will prioritize visual tracking when the obstacle distance reaches a safe threshold, which triggers the LiDAR obstacle avoidance program [40,41], α rows = 0, the steering angle of the robot is Equation (14).…”

Section: Heading Control Of the Robotmentioning

confidence: 99%

“…Similarly, robot navigation in obstacle environments remains a challenging problem, especially in the field of agriculture, where accurate navigation of robots in complex agricultural environments is a prerequisite for completing various tasks [14]. Gao et al discussed the solution to the navigation problem of Mobile Wheeled Robots (WMRs) in agricultural engineering and analyzed the navigation mechanism of WMRs in detail, but do not delve into their cost [15].…”

Section: Introductionmentioning

confidence: 99%

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Visual Navigation and Obstacle Avoidance Control for Agricultural Robots via LiDAR and Camera

Han,

Wu,

Luo

et al. 2023

Remote Sensing

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Obstacle avoidance control and navigation in unstructured agricultural environments are key to the safe operation of autonomous robots, especially for agricultural machinery, where cost and stability should be taken into account. In this paper, we designed a navigation and obstacle avoidance system for agricultural robots based on LiDAR and a vision camera. The improved clustering algorithm is used to quickly and accurately analyze the obstacle information collected by LiDAR in real time. Also, the convex hull algorithm is combined with the rotating calipers algorithm to obtain the maximum diameter of the convex polygon of the clustered data. Obstacle avoidance paths and course control methods are developed based on the danger zones of obstacles. Moreover, by performing color space analysis and feature analysis on the complex orchard environment images, the optimal H-component of HSV color space is selected to obtain the ideal vision-guided trajectory images based on mean filtering and corrosion treatment. Finally, the proposed algorithm is integrated into the Three-Wheeled Mobile Differential Robot (TWMDR) platform to carry out obstacle avoidance experiments, and the results show the effectiveness and robustness of the proposed algorithm. The research conclusion can achieve satisfactory results in precise obstacle avoidance and intelligent navigation control of agricultural robots.

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Section: Heading Control Of the Robotmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Visual Navigation and Obstacle Avoidance Control for Agricultural Robots via LiDAR and Camera

Han,

Wu,

Luo

et al. 2023

Remote Sensing

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show abstract

“…Lepej and Rakun [25] relied on a 2-D laser scanner to detect landmarks for real-time robot map building and localization in an apple orchard. Blok et al [26] used a probabilistic particle filter (PF) algorithm combined with LIDAR detection to achieve autonomous robot navigation within fruit tree rows by detecting tree rows. Jones, et al [27] used multilayer LIDAR to linearly track vehicle trajectories marked on an offline map of a kiwifruit orchard, and experimental results showed an accuracy of ±75 mm.…”

Section: Related Workmentioning

confidence: 99%

Development of a Combined Orchard Harvesting Robot Navigation System

et al. 2022

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Our research concerned the development of an autonomous robotic navigation system for orchard harvesting with a dual master-slave mode, the autonomous navigation tractor orchard transport robot being the master followed by a navigation orchard picking robot as the slave. This addresses the problem that in single master-slave navigation mode, agricultural combined harvesting equipment cannot stop repeatedly between rows of apple trees and drive continuously when turning. According to distances obtained from a global positioning system (GNSS), ground points were used to switch the navigation mode of the transport and picking robot. A cloth simulation filter (CSF) and random sample consensus (RANSAC) algorithm was used to obtain inter-row waypoints. The GNSS point was manually selected as the turn waypoint of the master and a kinematic model was used to compute the turn waypoints of the slave. Finally, we used a pure pursuit algorithm to track these waypoints sequentially to achieve master-slave navigation and ground head master-slave command navigation. The experimental results show that the data packet loss rate was less than 1.2% when the robot communicated in the orchard row within 50 m which meets the robot orchard communication requirements. The master-slave robot can achieve repeated stops in the row using follow navigation, which meets the demands of joint orchard harvesting. The maximum, minimum, mean and standard deviation of position deviation of the master robot were 5.3 cm, 0.8 cm, 2.4 cm, and 0.9 cm, respectively. The position deviations of the slave robot were larger than those of the master robot, with maximum, minimum, mean and standard deviation of 39.7 cm, 1.1 cm, 4.1 cm, and 5.6 cm, respectively. The maximum, minimum, mean and standard deviation of the following error between the master-slave robot were 4.4 cm, 0 cm, 1.3 cm, and 1 cm respectively. Concerning the ground head turn, the command navigation method allowed continuous turning, but the lateral deviation between robots was more than 0.3 m and less than 1 m, and the heading deviation was more than 10° and less than 90°.

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Robot navigation in orchards with localization based on Particle filter and Kalman filter

Blok

Boheemen

Evert

et al. 2019

Computers and Electronics in Agriculture

118

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Autonomous In-Row Navigation of an Orchard Robot with a 2D LIDAR Scanner and Particle Filter with a Laser-Beam Model

Cited by 5 publications

References 0 publications

Visual Navigation and Obstacle Avoidance Control for Agricultural Robots via LiDAR and Camera

Visual Navigation and Obstacle Avoidance Control for Agricultural Robots via LiDAR and Camera

Development of a Combined Orchard Harvesting Robot Navigation System

Robot navigation in orchards with localization based on Particle filter and Kalman filter

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